code: def kernel_cdf_lognorm(x, sample, bw):
      # Log-normal kernel for cumulative distribution, cdf, estimation
  
      # need shape-scale parameterization for scipy
      # not sure why JK picked this normalization, makes required bw small
      # maybe we should skip this transformation and just use bw
      # Funke and Kawka 2015 (table 1) use bw (or bw**2) corresponding to
      #    variance of normal pdf
      # bw = np.exp(bw_**2 / 4) - 1  # this is inverse transformation
      bw_ = np.sqrt(4 * np.log(1 + bw))
      return stats.lognorm.sf(sample, bw_, scale=x)
  
imports: import numpy
  from scipy import special, stats
label_desc: Use image node_id 24 for calling a global function with example usage: kernel_cdf_lognorm(x, sample, bw) and returns: stats